In retrospective change point analysis,
it is often more realistic to allow for multiple change points,
where the goal is to estimate both the total number and locations of the change points,
a.k.a. data segmentation.
While some earlier papers already obtained theoretical results
on the estimation of total number and locations of multiple change points
e.g. using information criteria [1]}, [2]},
the problem of detecting multiple change points in data sequences
experienced renewed interest in the statistical literature in 2010's.
This was driven by several factors,
including the emergence of scientific problems requiring high-precision and high-speed
segmentation algorithms: Such examples can be found in
genomics [3]}, [4]}, [5]}, [6]},
astronomy [7]}, neurophysiology [8]}, climatology [9]},
cyber-security [10]} and finance [11]}, to name but a few.
Accordingly, there has been a surge of interest for
computationally fast and statistically efficient methods for data segmentation,
and most effort in this direction has been devoted to
the problem of detecting multiple change points in the mean of univariate data.
In this article, we also take this as an important starting point
for developing change point methodologies in more complex situations.
We first review the methods for detection and localisation of
multiple change points in the mean of univariate data,
which we refer to as the canonical segmentation problem,
complementing the articles reviewing and comparing different change point detection algorithms
such as [12]}, [13]}, [14]}, [15]}.
